God, Human, Animal, Machine: Technology, Metaphor, and the Search for Meaning
Meghan O'Gieblyn
A strikingly original exploration of what it might mean to be authentically human in the age of artificial intelligence, from the author of the critically-acclaimed Interior States. "Meghan O’Gieblyn is a brilliant and humble philosopher, and her book is an explosively thought-provoking, candidly personal ride I wished never to end ... This book is such an original synthesis of ideas and disclosures. It introduces what will soon be called the O’Gieblyn genre of essay writing.” —Heidi Julavits, author of The Folded Clock For most of human history the world was a magical and enchanted place ruled by forces beyond our understanding. The rise of science and Descartes's division of mind from world made materialism our ruling paradigm, in the process asking whether our own consciousness—i.e., souls—might be illusions. Now the inexorable rise of technology, with artificial intelligences that surpass our comprehension and control, and the spread of digital metaphors for self-understanding, the core questions of existence—identity, knowledge, the very nature and purpose of life itself—urgently require rethinking. Meghan O'Gieblyn tackles this challenge with philosophical rigor, intellectual reach, essayistic verve, refreshing originality, and an ironic sense of contradiction.
Award History
0 wins · 1 total
| Award | Year | Result | Category / Notes |
|---|---|---|---|
| Los Angeles Times Book Prize for Science and TechnologyMajor | 2021 | Finalist | Secondary source |
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies
This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.
01 Central figures
None extracted with sufficient confidence.
02 Central places
None extracted with sufficient confidence.
03 Suggested argument
No argument inferred with sufficient confidence.
04 Reading orientation
52 / 100 academicTrade / academic crossover
An estimate of intended readership and scholarly apparatus—not quality or importance.
Confidence percentages are the model's own estimates. Profile confidence: 78%.